This invention relates to an image processing apparatus which utilizes a neural network to execute processing for restoring an original multivalued image from binarized image information.
One conventional method of executing processing for restoring an original multivalued image from a binarized image is to use a smoothing filter circuit which applies smoothing to an area having a rectangular shape. Another method that has been proposed is to store the results of processing by a smoothing filter in a ROM or RAM as a LUT (look-up table) beforehand in order to reduce circuitry and raise processing speed, and then refer to this table when image processing is performed.
FIG. 1 illustrates a prior-art example of a multiple-value converting apparatus using a LUT which performs the same function as a 3.times.3 filter. The apparatus includes a line buffer 701 comprising FIFO memories or the like. The line buffer 701 receives an input of binary data from an image input unit (not shown) and accumulates three raster lines of data. A data latch 702 latches three pixels of data for every line from among the three lines of data delivered by the line buffer 701. Binary image data corresponding to a 3.times.3 window is obtained from the data latch 702. This data of 3.times.3=9 bits is applied to a ROM-type LUT 703 as address data. The LUT 703, the content of which has been decided beforehand by the output of a smoothing filter, outputs multivalued data of 256 tones (eight bits) corresponding to the input data.
Various problems arise when relying upon such a method using a filter or LUT. For example, when it is attempted to execute processing to restore a binary image to a multivalued image by a 3.times.3 smoothing filter shown in FIG. 2A, half-tone image portions can be restored to multiple values with comparatively good results, but as shown on the right side of FIG. 3, "blurring" occurs at edge portions (binary images shown on the left side of FIG. 3), at which there is a marked difference in density, as at the fine-line and character portions or the like.
On the other hand, in a case where processing is executed by a smoothing filter which stresses a pixel of interest in order to emphasize fine-line portions, as shown in FIG. 2B, the aforesaid blurring at the fine-line portions is eliminated, but a drawback is that a grainy property peculiar to a binary image strongly remains at half-tone portions. As a consequence of this drawback, a difference in density of the kind shown on the right side of FIG. 4 develops at smooth portions such as background portions and portions corresponding to the human skin, as shown on the left side of FIG. 4.
A method contemplated to solve this problem is to discriminate among areas and to convert fine-line portions and image portions to multiple values separately. However, the state of the art is such that a method of image discrimination based upon binary image data has not yet been established.
The present assignee has proposed, in U.S. Ser. No. 673,240 (filed on Mar. 20, 1991), an image processing method using a neural network. This proposed method entails providing a rectangular window area the center of which is a pixel of interest with regard to restoration, and estimating multivalued data from the binary pattern of an input corresponding to this area by utilizing a neural network. With this method in which the aforesaid window is provided, however, good results cannot be obtained unless the window is large in size. A problem that arises when a large window is provided is that the pixels to be referred to are excessive in number. When a conversion into multiple values is performed using a neural network, the fact that there are a large number of pixels to be referred to results in hardware-related difficulties from the viewpoint of processing speed and the size of the circuitry.